1. Field of the Invention
The invention relates generally to neural systems, and more specifically, to determining the stability of synthetic, natural, or mixed neural systems in the context of a behavioral hyper space.
2. Introduction
Neural systems are mathematical or computational models consisting of an interconnected group of nodes, otherwise known as neurons or simple processing elements, which process information in a connectionist approach. Some neural systems may be constructed so as to adapt their structure based on internal or external factors. In order to create a neural system that demonstrates reasonable behavior, the neural system must have a certain level of complexity. Ideally, that complexity is stable. However, with additional complexity come additional stability problems. In humans, additional complexity may come in the form of psychological conditions or tendencies, such as Narcissistic Entitlement Syndrome, overly perfectionist tendencies, etc. In machines, the additional complexity comes from the various subsystems and/or the interactions between the various subsystems.
An example application of a complex neural system with many subsystems could be the robotic architecture called Autonomic Nano Technology Swarm (ANTS) described at http://ants.gsfc.nasa.gov. ANTS forms a complex neural system containing many subsystems such as Lower Level Neural System that provides security and safety, a Higher Level Neural System that provides more purposeful behavior such as problem solving, planning, or scheduling, an Evolvable Neural Interface to coordinate efforts between the higher and lower level subsystems, and the skeletal/muscular system of the frame itself. Some subsystems are complex neural systems in and of themselves.
Another example application of a neural system is the artificial intelligence “game”20Q which may be found at http://www.20q.net. 20Q employs a neural system to ask 20 questions about an item and guess what the item is at the end of the question period.
Indeed, a neural network is a particular software realization of just higher or heuristic level of the Neural Basis Function Synthetic Neural System (NBF SNS) which has already been demonstrated to be capable of very rapid learning and development.
One prior approach is to create a rule-driven system, but every rule-driven system will encounter exceptions to the rules and must be made adaptive. Prior systems address increasing instability with increasing complexity are qualitative and lack the precision needed to correct unstable systems. Prior systems also provide a rigorous approach to neural system stability analysis, attempting to catalog every possible state in the neural system, which results in a prohibitively high number of states. Such systems include requirements to identify unstable interactions between elements of neural systems and to provide guidance on their correction. Accordingly, what is needed in the art is a way to quantify stability analysis of synthetic and natural neural systems.